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Fix ci error in unet_plusplus.ipynb (#1573)
Fixes #1572 . Clean up long text outputs. ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` Signed-off-by: KumoLiu <yunl@nvidia.com>
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‎modules/network_contraints/unet_plusplus.ipynb‎

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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"BasicUNetPlusPlus features: (32, 32, 64, 128, 256, 32).\n",
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"BasicUNetPlusPlus(\n",
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" (conv_0_0): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(3, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" )\n",
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" )\n",
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" (conv_1): Convolution(\n",
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" (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" )\n",
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" )\n",
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" )\n",
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" (conv_1_0): Down(\n",
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" (max_pooling): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" )\n",
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" )\n",
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" (conv_1): Convolution(\n",
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" (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (conv_2_0): Down(\n",
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" (max_pooling): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(32, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" )\n",
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" )\n",
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" (conv_1): Convolution(\n",
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" (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" )\n",
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" (conv_3_0): Down(\n",
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" (max_pooling): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(64, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (conv_1): Convolution(\n",
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" (conv): Conv3d(128, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (conv_4_0): Down(\n",
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" (max_pooling): MaxPool3d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(128, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (conv): Conv3d(256, 256, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (upcat_0_1): UpCat(\n",
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" (upsample): UpSample(\n",
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" (deconv): ConvTranspose3d(32, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (conv_1): Convolution(\n",
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" (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (upcat_1_1): UpCat(\n",
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" (upsample): UpSample(\n",
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" (deconv): ConvTranspose3d(64, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
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" )\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(64, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (upcat_2_1): UpCat(\n",
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" (upsample): UpSample(\n",
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" (deconv): ConvTranspose3d(128, 64, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
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" )\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(128, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (conv_1): Convolution(\n",
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" (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (upcat_3_1): UpCat(\n",
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" (upsample): UpSample(\n",
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" (deconv): ConvTranspose3d(256, 128, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
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" (convs): TwoConv(\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(256, 128, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (N): BatchNorm3d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (conv_0): Convolution(\n",
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" (conv): Conv3d(96, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (conv): Conv3d(96, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (conv): Conv3d(64, 64, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (deconv): ConvTranspose3d(32, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
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" (convs): TwoConv(\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (upcat_0_4): UpCat(\n",
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" (upsample): UpSample(\n",
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" (deconv): ConvTranspose3d(32, 32, kernel_size=(2, 2, 2), stride=(2, 2, 2))\n",
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" (convs): TwoConv(\n",
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" (conv): Conv3d(160, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" (conv): Conv3d(32, 32, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))\n",
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" (adn): ADN(\n",
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" (N): BatchNorm3d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
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" (D): Dropout(p=0.0, inplace=False)\n",
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" (A): LeakyReLU(negative_slope=0.1, inplace=True)\n",
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" )\n",
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" )\n",
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" )\n",
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" (final_conv_0_1): Conv3d(32, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
462-
" (final_conv_0_2): Conv3d(32, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
463-
" (final_conv_0_3): Conv3d(32, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
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" (final_conv_0_4): Conv3d(32, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1))\n",
465-
")\n"
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]
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}
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],
122+
"outputs": [],
469123
"source": [
470124
"model = BasicUnetPlusPlus(\n",
471125
" spatial_dims=3,\n",

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